Clinical Decision-Making Processes in COVID-19 Pandemic: Changes and Effects

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Coronaviruses (CoV) and COVID-19 Pandemic".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 32974

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Foundations of Economic Analysis (PTUN), University of Salamanca, 37080 Salamanca, Spain
Interests: decision-making; social choice
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Special Issue Information

Dear Colleagues,

Recently, important changes in clinical decision-making processes have been carried out in response to the COVID-19 pandemic. These changes affect a wide variety of issues such as individual preferences, health modellings, surgical care, and pre-operative assessment.

This Special Issue aims to bring together state-of-the-art research and practical applications carried out in the context of clinical decision-making processes during the current pandemic. Topics of interest include, but are not limited to, the following:

  • Changes in surgical care and pre-operative assessment in response to the COVID-19 pandemic.
  • Changes in traditional healthcare: streamlining of treatments, telemedicine, and education in patient self-management in response to the COVID-19 pandemic.
  • Changes in shared decision-making processes in response to the COVID-19 pandemic.
  • Changes in hospital operational decision making in response to the COVID-19 pandemic.
  • Changes in clinical and ethical recommendations for decision making in nursing homes in response to the COVID-19 pandemic.
  • Changes in public health decision making in response to the COVID-19 pandemic.
  • New prediction models for clinical decision making in response to the COVID-19 pandemic.
  • Decision making in relation to the COVID-19 vaccine.
  • Ethical challenges of doctors and nurses during the COVID-19 pandemic.

Dr. Rocío De Andrés Calle
Guest Editor

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Keywords

  • Healthcare Preferences during COVID-19 pandemic
  • Socio-economic and health impact during COVID-19 pandemic
  • Inter-temporal Healthcare during COVID-19 pandemic
  • Clinical decisions under uncertainty during COVID-19 pandemic
  • Technology and Healthcare during COVID-19 pandemic

Published Papers (15 papers)

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Research

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14 pages, 2066 KiB  
Article
Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet
by Madini O. Alassafi, Ishtiaq Rasool Khan, Rayed AlGhamdi, Wajid Aziz, Abdulrahman A. Alshdadi, Mohamed M. Dessouky, Adel Bahaddad, Ali Altalbe and Nabeel Albishry
Healthcare 2023, 11(16), 2280; https://doi.org/10.3390/healthcare11162280 - 13 Aug 2023
Cited by 1 | Viewed by 977
Abstract
An aim of the analysis of biomedical signals such as heart rate variability signals, brain signals, oxygen saturation variability (OSV) signals, etc., is for the design and development of tools to extract information about the underlying complexity of physiological systems, to detect physiological [...] Read more.
An aim of the analysis of biomedical signals such as heart rate variability signals, brain signals, oxygen saturation variability (OSV) signals, etc., is for the design and development of tools to extract information about the underlying complexity of physiological systems, to detect physiological states, monitor health conditions over time, or predict pathological conditions. Entropy-based complexity measures are commonly used to quantify the complexity of biomedical signals; however novel complexity measures need to be explored in the context of biomedical signal classification. In this work, we present a novel technique that used Haar wavelets to analyze the complexity of OSV signals of subjects during COVID-19 infection and after recovery. The data used to evaluate the performance of the proposed algorithms comprised recordings of OSV signals from 44 COVID-19 patients during illness and after recovery. The performance of the proposed technique was compared with four, scale-based entropy measures: multiscale entropy (MSE); multiscale permutation entropy (MPE); multiscale fuzzy entropy (MFE); multiscale amplitude-aware permutation entropy (MAMPE). Preliminary results of the pilot study revealed that the proposed algorithm outperformed MSE, MPE, MFE, and MMAPE in terms of better accuracy and time efficiency for separating during and after recovery the OSV signals of COVID-19 subjects. Further studies are needed to evaluate the potential of the proposed algorithm for large datasets and in the context of other biomedical signal classifications. Full article
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16 pages, 319 KiB  
Article
Clinical Disease Characteristics and Treatment Trajectories Associated with Mortality among COVID-19 Patients in Punjab, Pakistan
by Muhammad Zeeshan Munir, Amer Hayat Khan and Tahir Mehmood Khan
Healthcare 2023, 11(8), 1192; https://doi.org/10.3390/healthcare11081192 - 21 Apr 2023
Viewed by 3098
Abstract
Background: Data on Pakistani COVID-19 patient mortality predictors is limited. It is essential to comprehend the relationship between disease characteristics, medications used, and mortality for better patient outcomes. Methods: The medical records of confirmed cases in the Lahore and Sargodha districts were examined [...] Read more.
Background: Data on Pakistani COVID-19 patient mortality predictors is limited. It is essential to comprehend the relationship between disease characteristics, medications used, and mortality for better patient outcomes. Methods: The medical records of confirmed cases in the Lahore and Sargodha districts were examined using a two-stage cluster sampling from March 2021 to March 2022. Demographics, signs and symptoms, laboratory findings, and pharmacological medications as mortality indicators were noted and analyzed. Results: A total of 288 deaths occurred out of the 1000 cases. Death rates were higher for males and people over 40. Most of those who were mechanically ventilated perished (OR: 124.2). Dyspnea, fever, and cough were common symptoms, with a significant association amid SpO2 < 95% (OR: 3.2), RR > 20 breaths/min (OR: 2.5), and mortality. Patients with renal (OR: 2.3) or liver failure (OR: 1.5) were at risk. Raised C-reactive protein (OR: 2.9) and D-dimer levels were the indicators of mortality (OR: 1.6). The most prescribed drugs were antibiotics, (77.9%), corticosteroids (54.8%), anticoagulants (34%), tocilizumab (20.3%), and ivermectin (9.2%). Conclusions: Older males having breathing difficulties or signs of organ failure with raised C-reactive protein or D-dimer levels had high mortality. Antivirals, corticosteroids, tocilizumab, and ivermectin had better outcomes; antivirals were associated with lower mortality risk. Full article
15 pages, 1042 KiB  
Article
Evaluation of Convalescent Plasma in the Management of Critically Ill COVID-19 Patients (with No Detectable Neutralizing Antibodies Nab) in Kashmir, India
by Ahmed M. E. Elkhalifa, Showkat Ul Nabi, Naveed Nazir Shah, Khurshid Ahmad Dar, Syed Quibtiya, Showkeen Muzamil Bashir, Sofi Imtiyaz Ali, Syed Taifa and Iqra Hussain
Healthcare 2023, 11(3), 317; https://doi.org/10.3390/healthcare11030317 - 20 Jan 2023
Cited by 2 | Viewed by 1466
Abstract
Background: For centuries, convalescent plasma (CP) has been recommended to treat a diverse set of viral diseases. Therefore, the present study was undertaken to evaluate the effectiveness of CP in critically ill COVID-19 patients. Methods and Materials: From 23 March 2021 to 29 [...] Read more.
Background: For centuries, convalescent plasma (CP) has been recommended to treat a diverse set of viral diseases. Therefore, the present study was undertaken to evaluate the effectiveness of CP in critically ill COVID-19 patients. Methods and Materials: From 23 March 2021 to 29 December 2021, an open-label, prospective cohort, single-centre study was conducted at Chest Disease Hospital, Jammu and Kashmir, Srinagar. Patients with severe manifestation of coronavirus disease 2019 (COVID-19) under BST (best standard treatment) +CP were prospectively observed in order to evaluate effectiveness of CP therapy and historical control under BST were used as the control group Results: A total of 1667 patients were found positive for COVID-19. Of these, 873 (52.4%), 431 (28.8%), and 363 (21.8%) were moderately, severely, and critically ill, respectively. On 35th day post-infusion of CP, all-cause mortality was higher in the BST (best standard treatment) +CP group 12 (37.5%) compared to 127 (35%) in the BST group with an odds ratio (OR) of 1.4 and hazard ratio (HR) (95% CI: 1.08–1.79, p = 0.06). Similarly, 7 (21.9) patients in the BST+CP group and 121 (33.3) patients in the BST group showed the transition from critically ill to moderate disease with subhazard ratio (s-HR 1.37) (95% CI: 1.03–2.9). Conclusions: In the present study, we could not find any significant difference in the CP group and BST +CP in primary outcome of reducing all-cause mortality in critically ill patients with negligible Nabs levels. However, beneficial results were observed with use of CP in a limited number of secondary outcomes which includes days of hospitalization, negative conversion of SARS-CoV-2 on basis of RT-PCR on 7th day and 14th day, need for invasive mechanical ventilation on 14th day post-CP treatment, and resolution of shortness of breath. Full article
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27 pages, 2192 KiB  
Article
Time-Series Analysis and Healthcare Implications of COVID-19 Pandemic in Saudi Arabia
by Rafat Zrieq, Souad Kamel, Sahbi Boubaker, Fahad D. Algahtani, Mohamed Ali Alzain, Fares Alshammari, Fahad Saud Alshammari, Badr Khalaf Aldhmadi, Suleman Atique, Mohammad A. A. Al-Najjar and Sandro C. Villareal
Healthcare 2022, 10(10), 1874; https://doi.org/10.3390/healthcare10101874 - 26 Sep 2022
Cited by 3 | Viewed by 2122
Abstract
The first case of coronavirus disease 2019 (COVID-19) in Saudi Arabia was reported on 2 March 2020. Since then, it has progressed rapidly and the number of cases has grown exponentially, reaching 788,294 cases on 22 June 2022. Accurately analyzing and predicting the [...] Read more.
The first case of coronavirus disease 2019 (COVID-19) in Saudi Arabia was reported on 2 March 2020. Since then, it has progressed rapidly and the number of cases has grown exponentially, reaching 788,294 cases on 22 June 2022. Accurately analyzing and predicting the spread of new COVID-19 cases is critical to develop a framework for universal pandemic preparedness as well as mitigating the disease’s spread. To this end, the main aim of this paper is first to analyze the historical data of the disease gathered from 2 March 2020 to 20 June 2022 and second to use the collected data for forecasting the trajectory of COVID-19 in order to construct robust and accurate models. To the best of our knowledge, this study is the first that analyzes the outbreak of COVID-19 in Saudi Arabia for a long period (more than two years). To achieve this study aim, two techniques from the data analytics field, namely the auto-regressive integrated moving average (ARIMA) statistical technique and Prophet Facebook machine learning technique were investigated for predicting daily new infections, recoveries and deaths. Based on forecasting performance metrics, both models were found to be accurate and robust in forecasting the time series of COVID-19 in Saudi Arabia for the considered period (the coefficient of determination for example was in all cases more than 0.96) with a small superiority of the ARIMA model in terms of the forecasting ability and of Prophet in terms of simplicity and a few hyper-parameters. The findings of this study have yielded a realistic picture of the disease direction and provide useful insights for decision makers so as to be prepared for the future evolution of the pandemic. In addition, the results of this study have shown positive healthcare implications of the Saudi experience in fighting the disease and the relative efficiency of the taken measures. Full article
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16 pages, 900 KiB  
Article
Sex-Specific Association between Underlying Diseases and the Severity and Mortality Due to COVID-19 Infection: A Retrospective Observational Cohort Analysis of Clinical Epidemiological Information Collected by the Korea Disease Control and Prevention Agency
by Hwayeong Oh, Roeul Kim and Woojin Chung
Healthcare 2022, 10(10), 1846; https://doi.org/10.3390/healthcare10101846 - 23 Sep 2022
Cited by 2 | Viewed by 2351
Abstract
This study is a retrospective observational cohort analysis aiming to explore the relationship between underlying disease and the severity and mortality rate of coronavirus disease (COVID-19) by sex. As sample subjects, 5077 confirmed COVID-19 patients were selected. The dependent variable was each patient’s [...] Read more.
This study is a retrospective observational cohort analysis aiming to explore the relationship between underlying disease and the severity and mortality rate of coronavirus disease (COVID-19) by sex. As sample subjects, 5077 confirmed COVID-19 patients were selected. The dependent variable was each patient’s clinical severity, dichotomized into two groups: clinical non-severity group and clinical severity group (including death group). Eleven underlying diseases were considered variables of interest, and each was dichotomized. Binary multivariate logistic regression model analyses were performed. Our results showed that the proportion of male patients (7.1%) in the clinical severity group was significantly higher than that of female patients (4.5%) and that the risk of being in the clinical severity group was higher in patients with specific underlying diseases. The underlying diseases varied: in males, rheumatism and autoimmune (adjusted odds ratio (aOR) = 6.69, 95% confidence interval (CI) = 1.60–27.98), dementia (aOR = 4.09, 95% CI = 2.14–7.82), cancer (aOR = 2.69, 95% CI = 1.27–5.69), and diabetes mellitus (aOR = 1.81, 95% CI = 1.18–2.77); in females, chronic kidney disease (aOR = 5.09, 95% CI = 1.87–13.86), dementia (aOR = 3.08, 95% CI = 1.18–5.23), diabetes mellitus (aOR = 1.87, 95% CI = 1.15–3.02), and hypertension (aOR = 1.73, 95% CI = 1.08–2.78). This study identified certain underlying diseases related to the high risk of being in clinically severe conditions and found that they differ between sexes. Prevention and treatment measure should be developed to reduce severity or mortality in confirmed COVID-19, based on underlying diseases and sex. However, further in-depth research is required to explore whether the findings and suggestions of this study can be generalized to other countries. Full article
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12 pages, 998 KiB  
Article
Predictors of Basic Activity in Daily Living and Length of Hospitalization in Patients with COVID-19
by Ting-Jie I, Yu-Lin Tsai and Yuan-Yang Cheng
Healthcare 2022, 10(8), 1589; https://doi.org/10.3390/healthcare10081589 - 22 Aug 2022
Cited by 1 | Viewed by 1588
Abstract
Background: Patients recovered from COVID-19 often suffer from the sequelae of the disease, which can hinder the patients’ activity in daily living. Early recognition of the patients at risk of prolonged hospitalization and impaired physical functioning is crucial for early intervention. We aim [...] Read more.
Background: Patients recovered from COVID-19 often suffer from the sequelae of the disease, which can hinder the patients’ activity in daily living. Early recognition of the patients at risk of prolonged hospitalization and impaired physical functioning is crucial for early intervention. We aim to identify the predictors of prolonged hospitalization and impaired activity in daily living in this study. Methods: COVID-19 patients hospitalized in a medical center were divided into two groups according to the Barthel index three months after discharge and the median length of hospital stay, respectively. Chi-square test and Mann–Whitney U test were performed to check the differences between the two groups in patient characteristics as well as hematology tests at the emergency department, the intensive care unit mobility scale (ICUMS), and the medical research council sum score (MRCSS). Logistic regression and the receiver operating characteristic curve analysis were further performed for the factors with significant differences between the two groups. Results: Both ICUMS and MRCSS showed significant differences between the groups. The ICUMS had an odds ratio of 0.61 and the MRCSS of 0.93 in predicting a Barthel index score less than 100 three months after discharge. The MRCSS had an odds ratio of 0.82 in predicting a prolonged length of hospital stay. Conclusion: Both ICUMS and MRCSS upon admission are predictive of a Barthel index score of less than 100 three months after discharge. On the other hand, only MRCSS has predictive value of a prolonged hospitalization. Full article
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16 pages, 4735 KiB  
Article
Enhanced Gravitational Search Optimization with Hybrid Deep Learning Model for COVID-19 Diagnosis on Epidemiology Data
by Mahmoud Ragab, Hani Choudhry, Amer H. Asseri, Sami Saeed Binyamin and Mohammed W. Al-Rabia
Healthcare 2022, 10(7), 1339; https://doi.org/10.3390/healthcare10071339 - 19 Jul 2022
Cited by 3 | Viewed by 1966
Abstract
Effective screening provides efficient and quick diagnoses of COVID-19 and could alleviate related problems in the health care system. A prediction model that combines multiple features to assess contamination risks was established in the hope of supporting healthcare workers worldwide in triaging patients, [...] Read more.
Effective screening provides efficient and quick diagnoses of COVID-19 and could alleviate related problems in the health care system. A prediction model that combines multiple features to assess contamination risks was established in the hope of supporting healthcare workers worldwide in triaging patients, particularly in situations with limited health care resources. Furthermore, a lack of diagnosis kits and asymptomatic cases can lead to missed or delayed diagnoses, exposing visitors, medical staff, and patients to 2019-nCoV contamination. Non-clinical techniques including data mining, expert systems, machine learning, and other artificial intelligence technologies have a crucial role to play in containment and diagnosis in the COVID-19 outbreak. This study developed Enhanced Gravitational Search Optimization with a Hybrid Deep Learning Model (EGSO-HDLM) for COVID-19 diagnoses using epidemiology data. The major aim of designing the EGSO-HDLM model was the identification and classification of COVID-19 using epidemiology data. In order to examine the epidemiology data, the EGSO-HDLM model employed a hybrid convolutional neural network with a gated recurrent unit based fusion (HCNN-GRUF) model. In addition, the hyperparameter optimization of the HCNN-GRUF model was improved by the use of the EGSO algorithm, which was derived by including the concepts of cat map and the traditional GSO algorithm. The design of the EGSO algorithm helps in reducing the ergodic problem, avoiding premature convergence, and enhancing algorithm efficiency. To demonstrate the better performance of the EGSO-HDLM model, experimental validation on a benchmark dataset was performed. The simulation results ensured the enhanced performance of the EGSO-HDLM model over recent approaches. Full article
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14 pages, 936 KiB  
Article
COVID-19 Intensive Care—Evaluation of Public Information Sources and Current Standards of Care in German Intensive Care Units: A Cross Sectional Online Survey on Intensive Care Staff in Germany
by Anne Werner, Maria Popp, Falk Fichtner, Christopher Holzmann-Littig, Peter Kranke, Anke Steckelberg, Julia Lühnen, Lisa Marie Redlich, Steffen Dickel, Clemens Grimm, Onnen Moerer, Monika Nothacker and Christian Seeber
Healthcare 2022, 10(7), 1315; https://doi.org/10.3390/healthcare10071315 - 15 Jul 2022
Cited by 1 | Viewed by 1493
Abstract
Backround: In February 2021, the first formal evidence and consensus-based (S3) guidelines for the inpatient treatment of patients with COVID-19 were published in Germany and have been updated twice during 2021. The aim of the present study is to re-evaluate the dissemination [...] Read more.
Backround: In February 2021, the first formal evidence and consensus-based (S3) guidelines for the inpatient treatment of patients with COVID-19 were published in Germany and have been updated twice during 2021. The aim of the present study is to re-evaluate the dissemination pathways and strategies for ICU staff (first evaluation in December 2020 when previous versions of consensus-based guidelines (S2k) were published) and question selected aspects of guideline adherence of standard care for patients with COVID-19 in the ICU. Methods: We conducted an anonymous online survey among German intensive care staff from 11 October 2021 to 11 November 2021. We distributed the survey via e-mail in intensive care facilities and requested redirection to additional intensive care staff (snowball sampling). Results: There was a difference between the professional groups in the number, selection and qualitative assessment of information sources about COVID-19. Standard operating procedures were most frequently used by all occupational groups and received a high quality rating. Physicians preferred sources for active information search (e.g., medical journals), while nurses predominantly used passive consumable sources (e.g., every-day media). Despite differences in usage behaviour, the sources were rated similarly in terms of the quality of the information on COVID-19. The trusted organizations have not changed over time. The use of guidelines was frequently stated and highly recommended. The majority of the participants reported guideline-compliant treatment. Nevertheless, there were certain variations in the use of medication as well as the criteria chosen for discontinuing non-invasive ventilation (NIV) compared to guideline recommendations. Conclusions: An adequate external source of information for nursing staff is lacking, the usual sources of physicians are only appropriate for the minority of nursing staff. The self-reported use of guidelines is high. Full article
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17 pages, 10021 KiB  
Article
Leveraging Tweets for Artificial Intelligence Driven Sentiment Analysis on the COVID-19 Pandemic
by Nora A. Alkhaldi, Yousef Asiri, Aisha M. Mashraqi, Hanan T. Halawani, Sayed Abdel-Khalek and Romany F. Mansour
Healthcare 2022, 10(5), 910; https://doi.org/10.3390/healthcare10050910 - 13 May 2022
Cited by 8 | Viewed by 2410
Abstract
The COVID-19 pandemic has been a disastrous event that has elevated several psychological issues such as depression given abrupt social changes and lack of employment. At the same time, social scientists and psychologists have gained significant interest in understanding the way people express [...] Read more.
The COVID-19 pandemic has been a disastrous event that has elevated several psychological issues such as depression given abrupt social changes and lack of employment. At the same time, social scientists and psychologists have gained significant interest in understanding the way people express emotions and sentiments at the time of pandemics. During the rise in COVID-19 cases with stricter lockdowns, people expressed their sentiments on social media. This offers a deep understanding of human psychology during catastrophic events. By exploiting user-generated content on social media such as Twitter, people’s thoughts and sentiments can be examined, which aids in introducing health intervention policies and awareness campaigns. The recent developments of natural language processing (NLP) and deep learning (DL) models have exposed noteworthy performance in sentiment analysis. With this in mind, this paper presents a new sunflower optimization with deep-learning-driven sentiment analysis and classification (SFODLD-SAC) on COVID-19 tweets. The presented SFODLD-SAC model focuses on the identification of people’s sentiments during the COVID-19 pandemic. To accomplish this, the SFODLD-SAC model initially preprocesses the tweets in distinct ways such as stemming, removal of stopwords, usernames, link punctuations, and numerals. In addition, the TF-IDF model is applied for the useful extraction of features from the preprocessed data. Moreover, the cascaded recurrent neural network (CRNN) model is employed to analyze and classify sentiments. Finally, the SFO algorithm is utilized to optimally adjust the hyperparameters involved in the CRNN model. The design of the SFODLD-SAC technique with the inclusion of an SFO algorithm-based hyperparameter optimizer for analyzing people’s sentiments on COVID-19 shows the novelty of this study. The simulation analysis of the SFODLD-SAC model is performed using a benchmark dataset from the Kaggle repository. Extensive, comparative results report the promising performance of the SFODLD-SAC model over recent state-of-the-art models with maximum accuracy of 99.65%. Full article
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15 pages, 3828 KiB  
Article
An Automated Glowworm Swarm Optimization with an Inception-Based Deep Convolutional Neural Network for COVID-19 Diagnosis and Classification
by Ibrahim Abunadi, Amani Abdulrahman Albraikan, Jaber S. Alzahrani, Majdy M. Eltahir, Anwer Mustafa Hilal, Mohamed I. Eldesouki, Abdelwahed Motwakel and Ishfaq Yaseen
Healthcare 2022, 10(4), 697; https://doi.org/10.3390/healthcare10040697 - 8 Apr 2022
Cited by 19 | Viewed by 2019
Abstract
Recently, the COVID-19 epidemic has had a major impact on day-to-day life of people all over the globe, and it demands various kinds of screening tests to detect the coronavirus. Conversely, the development of deep learning (DL) models combined with radiological images is [...] Read more.
Recently, the COVID-19 epidemic has had a major impact on day-to-day life of people all over the globe, and it demands various kinds of screening tests to detect the coronavirus. Conversely, the development of deep learning (DL) models combined with radiological images is useful for accurate detection and classification. DL models are full of hyperparameters, and identifying the optimal parameter configuration in such a high dimensional space is not a trivial challenge. Since the procedure of setting the hyperparameters requires expertise and extensive trial and error, metaheuristic algorithms can be employed. With this motivation, this paper presents an automated glowworm swarm optimization (GSO) with an inception-based deep convolutional neural network (IDCNN) for COVID-19 diagnosis and classification, called the GSO-IDCNN model. The presented model involves a Gaussian smoothening filter (GSF) to eradicate the noise that exists from the radiological images. Additionally, the IDCNN-based feature extractor is utilized, which makes use of the Inception v4 model. To further enhance the performance of the IDCNN technique, the hyperparameters are optimally tuned using the GSO algorithm. Lastly, an adaptive neuro-fuzzy classifier (ANFC) is used for classifying the existence of COVID-19. The design of the GSO algorithm with the ANFC model for COVID-19 diagnosis shows the novelty of the work. For experimental validation, a series of simulations were performed on benchmark radiological imaging databases to highlight the superior outcome of the GSO-IDCNN technique. The experimental values pointed out that the GSO-IDCNN methodology has demonstrated a proficient outcome by offering a maximal sensy of 0.9422, specy of 0.9466, precn of 0.9494, accy of 0.9429, and F1score of 0.9394. Full article
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25 pages, 41827 KiB  
Article
A Pathfinding Algorithm for Lowering Infection Exposure of Healthcare Personnel Working in Makeshift Hospitals
by Braxton Rolle, Ravi Kiran and Jeremy Straub
Healthcare 2022, 10(2), 344; https://doi.org/10.3390/healthcare10020344 - 10 Feb 2022
Cited by 2 | Viewed by 1918
Abstract
Due to the recent COVID-19 outbreak, makeshift (MS) hospitals have become an important feature in healthcare systems worldwide. Healthcare personnel (HCP) need to be able to navigate quickly, effectively, and safely to help patients, while still maintaining their own well-being. In this study, [...] Read more.
Due to the recent COVID-19 outbreak, makeshift (MS) hospitals have become an important feature in healthcare systems worldwide. Healthcare personnel (HCP) need to be able to navigate quickly, effectively, and safely to help patients, while still maintaining their own well-being. In this study, a pathfinding algorithm to help HCP navigate through a hospital safely and effectively is developed and verified. Tests are run using a discretized 2D grid as a representation of an MS hospital plan, and total distance traveled and total exposure to disease are measured. The influence of the size of the 2D grid units, the shape of these units, and degrees of freedom in the potential movement of the HCP are investigated. The algorithms developed are designed to be used in MS hospitals where airborne illness is prevalent and could greatly reduce the risk of illness in HCP. In this study, it was found that the quantum-based algorithm would generate paths that accrued 50–66% less total disease quantum than the shortest path algorithm with also about a 33–50% increase in total distance traveled. It was also found that the mixed path algorithm-generated paths accrued 33–50% less quantum, but only increased total distance traveled by 10–20%. Full article
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13 pages, 1458 KiB  
Article
Knowledge, Attitude, and Practice among Physical Therapists toward COVID-19 in the Kingdom of Saudi Arabia—A Cross-Sectional Study
by Adel Alshahrani, Ajay Prashad Gautam, Faisal Asiri, Irshad Ahmad, Mastour Saeed Alshahrani, Ravi Shankar Reddy, Mutasim D. Alharbi, Khalid Alkhathami, Hosam Alzahrani, Yasir S. Alshehri and Raee Alqhtani
Healthcare 2022, 10(1), 105; https://doi.org/10.3390/healthcare10010105 - 5 Jan 2022
Cited by 10 | Viewed by 2964
Abstract
To curb the COVID-19 pandemic, the knowledge, attitude, and practice (KAP) of preventive measures play an essential role, and healthcare workers have had to endure a burden to care for COVID-19 patients. Thus, this study aimed to assess the weight of the KAP [...] Read more.
To curb the COVID-19 pandemic, the knowledge, attitude, and practice (KAP) of preventive measures play an essential role, and healthcare workers have had to endure a burden to care for COVID-19 patients. Thus, this study aimed to assess the weight of the KAP of physiotherapists in Saudi Arabia during the COVID-19 pandemic. This was a cross-sectional study, where we circulated an online KAP questionnaire to 1179 physical therapists, and among those, 287 participated and completed the questionnaire. The collected responses were analyzed using descriptive statistics, t-test, ANOVA, correlation, and regression analyses, and p-value ≤ 0.05 was considered statistically significant. Both males and females participated in almost equal numbers; most of the participants were <40 years, had a bachelor’s level of education, and were from the central region of Saudi Arabia. Social media and the internet were the primary sources of COVID-19-related information (74.6%). Knowledge components A (92%) and B (73.9%) were excellent among most participants. Approximately half of the participants (50.5%) had a moderate attitude toward COVID-19, and regarding the practice component, most participants (74.6%) scored moderately. Correlation analysis showed a low positive relationship between knowledge A, attitude, and practice components. Still, there was a very low positive relationship between knowledge B, attitude, and practice components, but both were statistically significant. Our study showed that physical therapists in Saudi Arabia exhibit good knowledge, attitude, and practice toward COVID-19. Full article
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Review

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20 pages, 1565 KiB  
Review
Out-of-Hospital Cardiac Arrest during the COVID-19 Pandemic: A Systematic Review
by Amreen Aijaz Husain, Uddipak Rai, Amlan Kanti Sarkar, V. Chandrasekhar and Mohammad Farukh Hashmi
Healthcare 2023, 11(2), 189; https://doi.org/10.3390/healthcare11020189 - 8 Jan 2023
Cited by 5 | Viewed by 3021
Abstract
Objective: Out-of-hospital cardiac arrest (OHCA) is a prominent cause of death worldwide. As indicated by the high proportion of COVID-19 suspicion or diagnosis among patients who had OHCA, this issue could have resulted in multiple fatalities from coronavirus disease 2019 (COVID-19) occurring [...] Read more.
Objective: Out-of-hospital cardiac arrest (OHCA) is a prominent cause of death worldwide. As indicated by the high proportion of COVID-19 suspicion or diagnosis among patients who had OHCA, this issue could have resulted in multiple fatalities from coronavirus disease 2019 (COVID-19) occurring at home and being counted as OHCA. Methods: We used the MeSH term “heart arrest” as well as non-MeSH terms “out-of-hospital cardiac arrest, sudden cardiac death, OHCA, cardiac arrest, coronavirus pandemic, COVID-19, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).” We conducted a literature search using these search keywords in the Science Direct and PubMed databases and Google Scholar until 25 April 2022. Results: A systematic review of observational studies revealed OHCA and mortality rates increased considerably during the COVID-19 pandemic compared to the same period of the previous year. A temporary two-fold rise in OHCA incidence was detected along with a drop in survival. During the pandemic, the community’s response to OHCA changed, with fewer bystander cardiopulmonary resuscitations (CPRs), longer emergency medical service (EMS) response times, and worse OHCA survival rates. Conclusions: This study’s limitations include a lack of a centralised data-gathering method and OHCA registry system. If the chain of survival is maintained and effective emergency ambulance services with a qualified emergency medical team are given, the outcome for OHCA survivors can be improved even more. Full article
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10 pages, 1736 KiB  
Review
The Aftermath of the COVID-19 Crisis in Saudi Arabia: Respiratory Rehabilitation Recommendations by Physical Therapists
by Ravi Shankar Reddy, Ajay Prashad Gautam, Jaya Shanker Tedla, Arthur Sá Ferreira, Luis Felipe Fonseca Reis, Kalyana Chakravarthy Bairapareddy, Venkata Nagaraj Kakaraparthi and Kumar Gular
Healthcare 2021, 9(11), 1560; https://doi.org/10.3390/healthcare9111560 - 16 Nov 2021
Cited by 5 | Viewed by 2412
Abstract
Since late 2019, the number of COVID-19 patients has gradually increased in certain regions as consecutive waves of infections hit countries. Whenever this wave hits the corresponding areas, the entire healthcare system must respond quickly to curb the diseases, morbidities, and mortalities in [...] Read more.
Since late 2019, the number of COVID-19 patients has gradually increased in certain regions as consecutive waves of infections hit countries. Whenever this wave hits the corresponding areas, the entire healthcare system must respond quickly to curb the diseases, morbidities, and mortalities in intensive care settings. The healthcare team involved in COVID-19 patients’ care must work tirelessly without having breaks. Our understanding of COVID-19 is limited as new challenges emerge with new COVID-19 variants appearing in different world regions. Though medical therapies are finding solutions to deal with the disease, there are few recommendations for respiratory rehabilitation therapies. A group of respiratory rehabilitation care professionals in Saudi Arabia and international experts have agreed with the World Health bodies such as the World Health Organization (WHO) on the treatment and rehabilitation of patients with COVID-19. Professionals participating in COVID-19 patient treatment, rehabilitation, and recovery formulated respiratory rehabilitation guidelines based on the DELPHI Method, combining scientific research and personal practical experience. As a result, it is envisaged that the number of individuals in the region suffering from respiratory ailments due to post-COVID-19 will decrease. This narrative review and clinical expertise guidelines may give physiotherapists acceptable and standard clinical guideline protocols for treating COVID-19 patients. Full article
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5 pages, 953 KiB  
Case Report
Versatility of Intermittent Abdominal Pressure Ventilation in a Case of Complicated Restrictive Respiratory Failure and COVID-19
by Francesca Simioli, Anna Annunziata, Antonietta Coppola, Ediva Myriam Borriello, Sara Spinelli and Giuseppe Fiorentino
Healthcare 2022, 10(6), 1012; https://doi.org/10.3390/healthcare10061012 - 31 May 2022
Viewed by 1559
Abstract
Background: The intermittent abdominal pressure ventilation (IAPV) is a non-invasive ventilation (NIV) technique that avoids facial interfaces and is a diurnal ventilatory support alternative for neuromuscular patients during stable chronic phases of the disease. Coronavirus disease 2019 (COVID-19) is a novel infection possibly [...] Read more.
Background: The intermittent abdominal pressure ventilation (IAPV) is a non-invasive ventilation (NIV) technique that avoids facial interfaces and is a diurnal ventilatory support alternative for neuromuscular patients during stable chronic phases of the disease. Coronavirus disease 2019 (COVID-19) is a novel infection possibly causing acute respiratory distress syndrome (ARDS). Neuromuscular diseases (NMD) and preexisting respiratory failure can be exacerbated by respiratory infection and progress to severe disease and ICU admission with a poor prognosis. Aim: To report on the versatility and feasibility of IAPV in acute restrictive respiratory failure exacerbated by COVID-19. Patient: We describe the case of a 33-year-old man with spastic tetraparesis, kyphoscoliosis, and impaired cough, eventually leading to a restrictive ventilation pattern. COVID-19 exacerbated respiratory failure and seizures. An NIV trial failed because of inadequate interface adhesion and intolerance. During NIV, dyspnea and seizures worsened. He underwent a high flow nasal cannula (HFNC) with a fluctuating benefit on gas exchange. IAPV was initiated and although there was a lack of cooperation and inability to sit; the compliance was good and a progressive improvement of gas exchange, respiratory rate, and dyspnea was observed.Conclusions: IAPV is a versatile type of NIV that can be adopted in complicated restrictive respiratory failure. COVID-19 exacerbates preexisting conditions and is destined to be a disease of frailty. COVID-19 is not a contraindication to IAPV and this kind of ventilation can be employed in selected cases in a specialistic setting. Moreover, this report suggests that IAPV is safe when used in combination with HFNC. This hybrid approach provides the opportunity to benefit from both therapies, and, in this particular case, prevented the intubation with all connected risks. Full article
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